tldr-code

parcadei/continuous-claude-v3 · updated Apr 8, 2026

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$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill tldr-code
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summary

Token-efficient code analysis. 95% savings vs raw file reads.

skill.md

TLDR-Code: Complete Reference

Token-efficient code analysis. 95% savings vs raw file reads.

Quick Reference

Task Command
File tree tldr tree src/
Code structure tldr structure . --lang python
Search code tldr search "pattern" .
Call graph tldr calls src/
Who calls X? tldr impact func_name .
Control flow tldr cfg file.py func
Data flow tldr dfg file.py func
Program slice tldr slice file.py func 42
Dead code tldr dead src/
Architecture tldr arch src/
Imports tldr imports file.py
Who imports X? tldr importers module_name .
Affected tests tldr change-impact --git
Type check tldr diagnostics file.py
Semantic search tldr semantic search "auth flow"

The 5-Layer Stack

Layer 1: AST         ~500 tokens   Function signatures, imports
Layer 2: Call Graph  +440 tokens   What calls what (cross-file)
Layer 3: CFG         +110 tokens   Complexity, branches, loops
Layer 4: DFG         +130 tokens   Variable definitions/uses
Layer 5: PDG         +150 tokens   Dependencies, slicing
───────────────────────────────────────────────────────────────
Total:              ~1,200 tokens  vs 23,000 raw = 95% savings

CLI Commands

Navigation

# File tree
tldr tree [path]
tldr tree src/ --ext .py .ts        # Filter extensions
tldr tree . --show-hidden           # Include hidden files

# Code structure (codemaps)
tldr structure [path] --lang python
tldr structure src/ --max 100       # Max files to analyze

Search

# Text search
tldr search <pattern> [path]
tldr search "def process" src/
tldr search "class.*Error" . --ext .py
tldr search "TODO" . -C 3           # 3 lines context
tldr search "func" . --max 50       # Limit results

# Semantic search (natural language)
tldr semantic search "authentication flow"
tldr semantic search "error handling" --k 10
tldr semantic search "database queries" --expand  # Include call graph

File Analysis

# Full file info
tldr extract <file>
tldr extract src/api.py
tldr extract src/api.py --class UserService      # Filter to class
tldr extract src/api.py --function process       # Filter to function
tldr extract src/api.py --method UserService.get # Filter to method

# Relevant context (follows call graph)
tldr context <entry> --project <path>
tldr context main --project src/ --depth 3
tldr context UserService.create --project . --lang typescript

Flow Analysis

# Control flow graph (complexity)
tldr cfg <file> <function>
tldr cfg src/processor.py process_data
# Returns: cyclomatic complexity, blocks, branches, loops

# Data flow graph (variable tracking)
tldr dfg <file> <function>
tldr dfg src/processor.py process_data
# Returns: where variables are defined, read, modified

# Program slice (what affects line X)
tldr slice <file> <function> <line>
tldr slice src/processor.py process_data 42
tldr slice src/processor.py process_data 42 --direction forward
tldr slice src/processor.py process_data 42 --var result

Codebase Analysis

# Build cross-file call graph
tldr calls [path]
tldr calls src/ --lang python

# Reverse call graph (who calls this function?)
tldr impact <func> [path]
tldr impact process_data src/ --depth 5
tldr impact authenticate . --file auth  # Filter by file

# Find dead/unreachable code
tldr dead [path]
tldr dead src/ --entry main cli test_  # Specify entry points
tldr dead . --lang typescript

# Detect architectural layers
tldr arch [path]
tldr arch src/ --lang python
# Returns: entry layer, middle layer, leaf layer, circular deps

Import Analysis

# Parse imports from file
tldr imports <file>
tldr imports src/api.py
tldr imports src/api.ts --lang typescript

# Reverse import lookup (who imports this module?)
tldr importers <module> [path]
tldr importers datetime src/
tldr importers UserService . --lang typescript

Quality & Testing

# Type check + lint
tldr diagnostics <file|path>
tldr diagnostics src/api.py
tldr diagnostics . --project              # Whole project
tldr diagnostics src/ --no-lint           # Type check only
tldr diagnostics src/ --format text       # Human-readable

# Find affected tests
tldr change-impact [files...]
tldr change-impact                        # Auto-detect (session/git)
tldr change-impact src/api.py             # Explicit files
tldr change-impact --session              # Session-modified files
tldr change-impact --git                  # Git diff files
tldr change-impact --git --git-base main  # Diff against branch
tldr change-impact --run                  # Actually run affected tests

Caching

# Pre-build call graph cache
tldr warm <path>
tldr warm src/ --lang python
tldr warm . --background                  # Build in background

# Build semantic index (one-time)
tldr semantic index [path]
tldr semantic index . --lang python
tldr semantic index . --model all-MiniLM-L6-v2  # Smaller model (80MB)

Daemon (Faster Queries)

The daemon holds indexes in memory for instant repeated queries.

Daemon Commands

# Start daemon (backgrounds automatically)
tldr daemon start
tldr daemon start --project /path/to/project

# Check status
tldr daemon status

# Stop daemon
tldr daemon stop

# Send raw command
tldr daemon query ping
tldr daemon query status

# Notify file change (for hooks)
tldr daemon notify <file>
tldr daemon notify src/api.py

Daemon Features

Feature Description
Auto-shutdown 30 minutes idle
Query caching SalsaDB memoization
Content hashing Skip unchanged files
Dirty tracking Incremental re-indexing
Cross-platform Unix sockets / Windows TCP

Daemon Socket Protocol

Send JSON to socket, receive JSON response:

// Request
{"cmd": "search", "pattern": "process", "max_results": 10}

// Response
{"status": "ok", "results": [...]}

All 22 daemon commands:

ping, status, shutdown, search, extract, impact, dead, arch,
cfg, dfg, slice, calls, warm, semantic, tree, structure,
context, imports, importers, notify, diagnostics, change_impact

Semantic Search (P6)

Natural language code search using embeddings.

Setup

# Build index (downloads model on first run)
tldr semantic index .

# Default model: bge-large-en-v1.5 (1.3GB, best quality)
# Smaller model: all-MiniLM-L6-v2 (80MB, faster)
tldr semantic index . --model all-MiniLM-L6-v2

Search

tldr semantic search "authentication flow"
tldr semantic search "error handling patterns" --k 10
tldr semantic search "database connection" --expand  # Follow call graph

Configuration

In .claude/settings.json:

{
  "semantic_search": {
    "enabled": true,
    "auto_reindex_threshold": 20,
    "model": "bge-large-en-v1.5"
  }
}

Languages Supported

Language AST Call Graph CFG DFG PDG
Python Yes Yes Yes Yes Yes
TypeScript Yes Yes Yes Yes Yes
JavaScript Yes Yes Yes Yes Yes
Go Yes Yes Yes Yes Yes
Rust Yes Yes Yes Yes Yes
Java Yes Yes - - -
C/C++ Yes Yes - - -
Ruby Yes - - - -
PHP Yes - - - -
Kotlin Yes - - - -
Swift Yes - - - -
C# Yes - - - -
Scala Yes - - - -
Lua Yes - - - -
Elixir Yes - - - -

Ignore Patterns

TLDR respects .tldrignore (gitignore syntax):

# .tldrignore
.venv/
how to use tldr-code

How to use tldr-code on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tldr-code
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill tldr-code

The skills CLI fetches tldr-code from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tldr-code

Reload or restart Cursor to activate tldr-code. Access the skill through slash commands (e.g., /tldr-code) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.653 reviews
  • Noor Diallo· Dec 16, 2024

    tldr-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ama Malhotra· Dec 12, 2024

    We added tldr-code from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yuki Okafor· Dec 8, 2024

    Useful defaults in tldr-code — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Emma Zhang· Nov 7, 2024

    We added tldr-code from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Hiroshi Johnson· Nov 3, 2024

    Useful defaults in tldr-code — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Noor Abbas· Nov 3, 2024

    tldr-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Noor Rahman· Oct 26, 2024

    Solid pick for teams standardizing on skills: tldr-code is focused, and the summary matches what you get after install.

  • Hiroshi Garcia· Oct 22, 2024

    tldr-code has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Emma Liu· Oct 22, 2024

    tldr-code is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Lucas Perez· Sep 5, 2024

    I recommend tldr-code for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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